Mostra el registre d'ítem simple
Quasi-automatic colon segmentation on T2-MRI images with low user effort
dc.contributor.author | Orellana, Bernat |
dc.contributor.author | Monclús Lahoya, Eva |
dc.contributor.author | Brunet Crossa, Pere |
dc.contributor.author | Navazo Álvaro, Isabel |
dc.contributor.author | Bendezú García, Álvaro |
dc.contributor.author | Azpiroz Vidaur, Fernando |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2019-01-29T10:52:10Z |
dc.date.available | 2019-09-20T00:25:47Z |
dc.date.issued | 2018 |
dc.identifier.citation | Orellana, B. [et al.]. Quasi-automatic colon segmentation on T2-MRI images with low user effort. A: International Conference on Medical Image Computing and Computer Assisted Intervention. "Medical Image Computing and Computer Assisted Intervention: MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018: proceedings, part II". Berlín: Springer, 2018, p. 638-647. |
dc.identifier.isbn | 978-3-030-00934-2 |
dc.identifier.uri | http://hdl.handle.net/2117/127790 |
dc.description.abstract | About 50% of the patients consulting a gastroenterology clinic report symptoms without detectable cause. Clinical researchers are interested in analyzing the volumetric evolution of colon segments under the effect of different diets and diseases. These studies require noninvasive abdominal MRI scans without using any contrast agent. In this work, we propose a colon segmentation framework designed to support T2-weighted abdominal MRI scans obtained from an unprepared colon. The segmentation process is based on an efficient and accurate quasiautomatic approach that drastically reduces the specialist interaction and effort with respect other state-of-the-art solutions, while decreasing the overall segmentation cost. The algorithm relies on a novel probabilistic tubularity filter, the detection of the colon medial line, probabilistic information extracted from a training set and a final unsupervised clustering. Experimental results presented show the benefits of our approach for clinical use. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
dc.subject.lcsh | Diagnostic imaging |
dc.subject.other | MRI Segmentation |
dc.subject.other | Medical Diagnosi |
dc.title | Quasi-automatic colon segmentation on T2-MRI images with low user effort |
dc.type | Conference report |
dc.subject.lemac | Imatgeria per al diagnòstic |
dc.contributor.group | Universitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.springerprofessional.de/en/medical-image-computing-and-computer-assisted-intervention-micca/16118870?tocPage=1 |
dc.rights.access | Open Access |
local.identifier.drac | 23617820 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Orellana, B.; Monclús, E.; Brunet, P.; Navazo, I.; Bendezú, Á.; Azpiroz, F. |
local.citation.contributor | International Conference on Medical Image Computing and Computer Assisted Intervention |
local.citation.pubplace | Berlín |
local.citation.publicationName | Medical Image Computing and Computer Assisted Intervention: MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018: proceedings, part II |
local.citation.startingPage | 638 |
local.citation.endingPage | 647 |